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                <p>爬虫是做什么的？是帮助我们来快速获取有效信息的。然而做过爬虫的人都知道，解析是个麻烦事。 比如一篇新闻吧，链接是这个：<a href="https://news.ifeng.com/c/7kQcQG2peWU，页面预览图如下：" target="_blank" rel="noopener">https://news.ifeng.com/c/7kQcQG2peWU，页面预览图如下：</a> <img src="https://ws2.sinaimg.cn/large/006tKfTcly1g0cwc0n5u9j30w10no4ho.jpg" alt="预览图"> 我们需要从页面中提取出标题、发布人、发布时间、发布内容、图片等内容。一般情况下我们需要怎么办？写规则。 那么规则都有什么呢？怼正则，怼 CSS 选择器，怼 XPath。我们需要对标题、发布时间、来源等内容做规则匹配，更有甚者再需要正则表达式来辅助一下。我们可能就需要用 re、BeautifulSoup、pyquery 等库来实现内容的提取和解析。 但如果我们有成千上万个不同样式的页面怎么办呢？它们来自成千上万个站点，难道我们还需要对他们一一写规则来匹配吗？这得要多大的工作量啊。另外这些万一弄不好还会解析有问题。比如正则表达式在某些情况下匹配不了了，CSS、XPath 选择器选错位了也会出现问题。 想必大家可能见过现在的浏览器有阅读模式，比如我们把这个页面用 Safari 浏览器打开，然后开启阅读模式，看看什么效果： <img src="https://ws4.sinaimg.cn/large/006tKfTcly1g0cwgg60joj30mg0odwtt.jpg" alt="Safari预览"> 页面一下子变得非常清爽，只保留了标题和需要读的内容。原先页面多余的导航栏、侧栏、评论等等的统统都被去除了。它怎么做到的？难道是有人在里面写好规则了？那当然不可能的事。其实，这里面就用到了智能化解析了。 那么本篇文章，我们就来了解一下页面的智能化解析的相关知识。</p>
                <h2 id="智能化解析"><a href="#智能化解析" class="headerlink" title="智能化解析"></a>智能化解析</h2>
                <p>所谓爬虫的智能化解析，顾名思义就是不再需要我们针对某一些页面来专门写提取规则了，我们可以利用一些算法来计算出来页面特定元素的位置和提取路径。比如一个页面中的一篇文章，我们可以通过算法计算出来，它的标题应该是什么，正文应该是哪部分区域，发布时间是什么等等。 其实智能化解析是非常难的一项任务，比如说你给人看一个网页的一篇文章，人可以迅速找到这篇文章的标题是什么，发布时间是什么，正文是哪一块，或者哪一块是广告位，哪一块是导航栏。但给机器来识别的话，它面临的是什么？仅仅是一系列的 HTML 代码而已。那究竟机器是怎么做到智能化提取的呢？其实这里面融合了多方面的信息。</p>
                <ul>
                  <li>比如标题。一般它的字号是比较大的，而且长度不长，位置一般都在页面上方，而且大部分情况下它应该和 title 标签里的内容是一致的。</li>
                  <li>比如正文。它的内容一般是最多的，而且会包含多个段落 p 或者图片 img 标签，另外它的宽度一般可能会占用到页面的三分之二区域，并且密度（字数除以标签数量）会比较大。</li>
                  <li>比如时间。不同语言的页面可能不同，但时间的格式是有限的，如 2019-02-20 或者 2019/02/20 等等，也有的可能是美式的记法，顺序不同，这些也有特定的模式可以识别。</li>
                  <li>比如广告。它的标签一般可能会带有 ads 这样的字样，另外大多数可能会处于文章底部、页面侧栏，并可能包含一些特定的外链内容。</li>
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                <p>另外还有一些特点就不再一一赘述了，这其中包含了区块位置、区块大小、区块标签、区块内容、区块疏密度等等多种特征，另外很多情况下还需要借助于视觉的特征，所以说这里面其实结合了算法计算、视觉处理、自然语言处理等各个方面的内容。如果能把这些特征综合运用起来，再经过大量的数据训练，是可以得到一个非常不错的效果的。</p>
                <h2 id="业界进展"><a href="#业界进展" class="headerlink" title="业界进展"></a>业界进展</h2>
                <p>未来的话，页面也会越来越多，页面的渲染方式也会发生很大的变化，爬虫也会越来越难做，智能化爬虫也将会变得越来越重要。 目前工业界，其实已经有落地的算法应用了。经过我的一番调研，目前发现有这么几种算法或者服务对页面的智能化解析做的比较好：</p>
                <ul>
                  <li>Diffbot，国外的一家专门来做智能化解析服务的公司，<a href="https://www.diffbot.com" target="_blank" rel="noopener">https://www.diffbot.com</a></li>
                  <li>Boilerpipe，Java 语言编写的一个页面解析算法，<a href="https://github.com/kohlschutter/boilerpipe" target="_blank" rel="noopener">https://github.com/kohlschutter/boilerpipe</a></li>
                  <li>Embedly，提供页面解析服务的公司，<a href="https://embed.ly/extract" target="_blank" rel="noopener">https://embed.ly/extract</a></li>
                  <li>Readability，是一个页面解析算法，但现在官方的服务已经关闭了，<a href="https://www.readability.com/" target="_blank" rel="noopener">https://www.readability.com/</a></li>
                  <li>Mercury，Readability 的替代品，<a href="https://mercury.postlight.com/" target="_blank" rel="noopener">https://mercury.postlight.com/</a></li>
                  <li>Goose，Java 语音编写的页面解析算法，<a href="https://github.com/GravityLabs/goose" target="_blank" rel="noopener">https://github.com/GravityLabs/goose</a></li>
                </ul>
                <p>那么这几种算法或者服务到底哪些好呢，Driffbot 官方曾做过一个对比评测，使用 Google 新闻的一些文章，使用不同的算法依次摘出其中的标题和文本，然后与真实标注的内容进行比较，比较的指标就是文字的准确率和召回率，以及根据二者计算出的 F1 分数。 其结果对比如下：</p>
                <p>Service/Software</p>
                <p><a href="https://www.diffbot.com/benefits/comparison/#definitions" target="_blank" rel="noopener">Precision</a></p>
                <p><a href="https://www.diffbot.com/benefits/comparison/#definitions" target="_blank" rel="noopener">Recall</a></p>
                <p><a href="https://www.diffbot.com/benefits/comparison/#definitions" target="_blank" rel="noopener">F1-Score</a></p>
                <p><strong>Diffbot</strong></p>
                <p>0.968</p>
                <p>0.978</p>
                <p>0.971</p>
                <p><a href="https://code.google.com/p/boilerpipe/" target="_blank" rel="noopener">Boilerpipe</a></p>
                <p>0.893</p>
                <p>0.924</p>
                <p>0.893</p>
                <p><a href="https://www.readability.com/" target="_blank" rel="noopener">Readability</a></p>
                <p>0.819</p>
                <p>0.911</p>
                <p>0.854</p>
                <p><a href="http://www.alchemyapi.com/" target="_blank" rel="noopener">AlchemyAPI</a></p>
                <p>0.876</p>
                <p>0.892</p>
                <p>0.850</p>
                <p><a href="http://embed.ly/" target="_blank" rel="noopener">Embedly</a></p>
                <p>0.786</p>
                <p>0.880</p>
                <p>0.822</p>
                <p><a href="https://github.com/GravityLabs/goose" target="_blank" rel="noopener">Goose</a></p>
                <p>0.498</p>
                <p>0.815</p>
                <p>0.608</p>
                <p>经过对比我们可以发现，Diffbot 的准确率和召回率都独占鳌头，其中的 F1 值达到了 0.97，可以说准确率非常高了。另外接下来比较厉害的就是 Boilerpipe 和 Readability，Goose 的表现则非常差，F1 跟其他的算法差了一大截。下面是几个算法的 F1 分数对比情况： <img src="https://ws2.sinaimg.cn/large/006tKfTcgy1g0d4cv31noj30yw0kstaa.jpg" alt="F1分数对比"> 有人可能好奇为什么 Diffbot 这么厉害？我也查询了一番。Diffbot 自 2010 年以来就致力于提取 Web 页面数据，并提供许多 API 来自动解析各种页面。其中他们的算法依赖于自然语言技术、机器学习、计算机视觉、标记检查等多种算法，并且所有的页面都会考虑到当前页面的样式以及可视化布局，另外还会分析其中包含的图像内容、CSS 甚至 Ajax 请求。另外在计算一个区块的置信度时还考虑到了和其他区块的关联关系，基于周围的标记来计算每个区块的置信度。 总之，Diffbot 也是一直致力于这一方面的服务，整个 Diffbot 就是页面解析起家的，现在也一直专注于页面解析服务，准确率高也就不足为怪了。 但它们的算法开源了吗？很遗憾，并没有，而且我也没有找到相关的论文介绍它们自己的具体算法。 所以，如果想实现这么好的效果，那就使用它们家的服务就好了。 接下来的内容，我们就来说说如何使用 Diffbot 来进行页面的智能解析。另外还有 Readability 算法也非常值得研究，我会写专门的文章来介绍 Readability 及其与 Python 的对接使用。</p>
                <h2 id="Diffbot-页面解析"><a href="#Diffbot-页面解析" class="headerlink" title="Diffbot 页面解析"></a>Diffbot 页面解析</h2>
                <p>首先我们需要注册一个账号，它有 15 天的免费试用，注册之后会获得一个 Developer Token，这就是使用 Diffbot 接口服务的凭证。 接下来切换到它的测试页面中，链接为：<a href="https://www.diffbot.com/dev/home/，我们来测试一下它的解析效果到底是怎样的。" target="_blank" rel="noopener">https://www.diffbot.com/dev/home/，我们来测试一下它的解析效果到底是怎样的。</a> 这里我们选择的测试页面就是上文所述的页面，链接为：<a href="https://news.ifeng.com/c/7kQcQG2peWU，API" target="_blank" rel="noopener">https://news.ifeng.com/c/7kQcQG2peWU，API</a> 类型选择 Article API，然后点击 Test Drive 按钮，接下来它就会出现当前页面的解析结果： <img src="https://ws1.sinaimg.cn/large/006tKfTcgy1g0d4u30pjij30v70lhq57.jpg" alt="结果"> 这时候我们可以看到，它帮我们提取出来了标题、发布时间、发布机构、发布机构链接、正文内容等等各种结果。而且目前来看都十分正确，时间也自动识别之后做了转码，是一个标准的时间格式。 接下来我们继续下滑，查看还有什么其他的字段，这里我们还可以看到有 html 字段，它和 text 不同的是，它包含了文章内容的真实 HTML 代码，因此图片也会包含在里面，如图所示： <img src="https://ws1.sinaimg.cn/large/006tKfTcgy1g0d4wnlzbhj30uv0k3490.jpg" alt="结果"> 另外最后面还有 images 字段，他以列表形式返回了文章套图及每一张图的链接，另外还有文章的站点名称、页面所用语言等等结果，如图所示： <img src="https://ws2.sinaimg.cn/large/006tKfTcgy1g0d4yai8qqj30uf0l0q7v.jpg" alt="结果"> 当然我们也可以选择 JSON 格式的返回结果，其内容会更加丰富，例如图片还返回了其宽度、高度、图片描述等等内容，另外还有各种其他的结果如面包屑导航等等结果，如图所示： <img src="https://ws3.sinaimg.cn/large/006tKfTcgy1g0d500i671j30tn0fx0ue.jpg" alt="结果"> 经过手工核对，发现其返回的结果都是完全正确的，准确率相当之高！ 所以说，如果你对准确率要求没有那么非常非常严苛的情况下，使用 Diffbot 的服务可以帮助我们快速地提取页面中所需的结果，省去了我们绝大多数的手工劳动，可以说是非常赞了。 但是，我们也不能总在网页上这么试吧。其实 Diffbot 也提供了官方的 API 文档，让我们来一探究竟。</p>
                <h2 id="Diffbot-API"><a href="#Diffbot-API" class="headerlink" title="Diffbot API"></a>Diffbot API</h2>
                <p>Driffbot 提供了多种 API，如 Analyze API、Article API、Disscussion API 等。 下面我们以 Article API 为例来说明一下它的用法，其官方文档地址为：<a href="https://www.diffbot.com/dev/docs/article/，API" target="_blank" rel="noopener">https://www.diffbot.com/dev/docs/article/，API</a> 调用地址为：</p>
                <figure class="highlight awk">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">https:<span class="regexp">//</span>api.diffbot.com<span class="regexp">/v3/</span>article</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>我们可以用 GET 方式来进行请求，其中的 Token 和 URL 都可以以参数形式传递给这个 API，其必备的参数有：</p>
                <ul>
                  <li>token：即 Developer Token</li>
                  <li>url：即要解析的 URL 链接</li>
                </ul>
                <p>另外它还有几个可选参数：</p>
                <ul>
                  <li>fields：用来指定返回哪些字段，默认已经有了一些固定字段，这个参数可以指定还可以额外返回哪些可选字段</li>
                  <li>paging：如果是多页文章的话，如果将这个参数设置为 false 则可以禁止多页内容拼接</li>
                  <li>maxTags：可以设置返回的 Tag 最大数量，默认是 10 个</li>
                  <li>tagConfidence：设置置信度的阈值，超过这个值的 Tag 才会被返回，默认是 0.5</li>
                  <li>discussion：如果将这个参数设置为 false，那么就不会解析评论内容</li>
                  <li>timeout：在解析的时候等待的最长时间，默认是 30 秒</li>
                  <li>callback：为 JSONP 类型的请求而设计的回调</li>
                </ul>
                <p>这里大家可能关注的就是 fields 字段了，在这里我专门做了一下梳理，首先是一些固定字段：</p>
                <ul>
                  <li>type：文本的类型，这里就是 article 了</li>
                  <li>title：文章的标题</li>
                  <li>text：文章的纯文本内容，如果是分段内容，那么其中会以换行符来分隔</li>
                  <li>html：提取结果的 HTML 内容</li>
                  <li>date：文章的发布时间，其格式为 RFC 1123</li>
                  <li>estimatedDate：如果日期时间不太明确，会返回一个预估的时间，如果文章超过两天或者没有发布日期，那么这个字段就不会返回</li>
                  <li>author：作者</li>
                  <li>authorUrl：作者的链接</li>
                  <li>discussion：评论内容，和 Disscussion API 返回结果一样</li>
                  <li>humanLanguage：语言类型，如英文还是中文等</li>
                  <li>numPages：如果文章是多页的，这个参数会控制最大的翻页拼接数目</li>
                  <li>nextPages：如果文章是多页的，这个参数可以指定文章后续链接</li>
                  <li>siteName：站点名称</li>
                  <li>publisherRegion：文章发布地区</li>
                  <li>publisherCountry：文章发布国家</li>
                  <li>pageUrl：文章链接</li>
                  <li>resolvedPageUrl：如果文章是从 pageUrl 重定向过来的，则返回此内容</li>
                  <li>tags：文章的标签或者文章包含的实体，根据自然语言处理技术和 DBpedia 计算生成，是一个列表，里面又包含了子字段：<ul>
                      <li>label：标签名</li>
                      <li>count：标签出现的次数</li>
                      <li>score：标签置信度</li>
                      <li>rdfTypes：如果实体可以由多个资源表示，那么则返回相关的 URL</li>
                      <li>type：类型</li>
                      <li>uri：Diffbot Knowledge Graph 中的实体链接</li>
                    </ul>
                  </li>
                  <li>images：文章中包含的图片</li>
                  <li>videos：文章中包含的视频</li>
                  <li>breadcrumb：面包屑导航信息</li>
                  <li>diffbotUri：Diffbot 内部的 URL 链接</li>
                </ul>
                <p>以上的预定字段就是如果可以返回那就会返回的字段，是不能定制化配置的，另外我们还可以通过 fields 参数来指定扩展如下可选字段：</p>
                <ul>
                  <li>quotes：引用信息</li>
                  <li>sentiment：文章的情感值，-1 到 1 之间</li>
                  <li>links：所有超链接的顶级链接</li>
                  <li>querystring：请求的参数列表</li>
                </ul>
                <p>好，以上便是这个 API 的用法，大家可以申请之后使用这个 API 来做智能化解析了。 下面我们用一个实例来看一下这个 API 的用法，代码如下：</p>
                <figure class="highlight routeros">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">import requests, json</span><br><span class="line"></span><br><span class="line">url = <span class="string">'https://api.diffbot.com/v3/article'</span></span><br><span class="line">params = &#123;</span><br><span class="line">    <span class="string">'token'</span>: <span class="string">'77b41f6fbb24496d5113d528306528fa'</span>,</span><br><span class="line">    <span class="string">'url'</span>: <span class="string">'https://news.ifeng.com/c/7kQcQG2peWU'</span>,</span><br><span class="line">    <span class="string">'fields'</span>: <span class="string">'meta'</span> </span><br><span class="line">&#125;</span><br><span class="line">response = requests.<span class="builtin-name">get</span>(url, <span class="attribute">params</span>=params)</span><br><span class="line"><span class="builtin-name">print</span>(json.dumps(response.json(), <span class="attribute">indent</span>=2, <span class="attribute">ensure_ascii</span>=<span class="literal">False</span>))</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>这里首先定义了 API 的链接，然后指定了 params 参数，即 GET 请求参数。 参数中包含了必选的 token、url 字段，也设置了可选的 fields 字段，其中 fields 为可选的扩展字段 meta 标签。 我们来看下运行结果，结果如下：</p>
                <figure class="highlight json">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line">&#123;</span><br><span class="line">  <span class="attr">"request"</span>: &#123;</span><br><span class="line">    <span class="attr">"pageUrl"</span>: <span class="string">"https://news.ifeng.com/c/7kQcQG2peWU"</span>,</span><br><span class="line">    <span class="attr">"api"</span>: <span class="string">"article"</span>,</span><br><span class="line">    <span class="attr">"fields"</span>: <span class="string">"sentiment, meta"</span>,</span><br><span class="line">    <span class="attr">"version"</span>: <span class="number">3</span></span><br><span class="line">  &#125;,</span><br><span class="line">  <span class="attr">"objects"</span>: [</span><br><span class="line">    &#123;</span><br><span class="line">      <span class="attr">"date"</span>: <span class="string">"Wed, 20 Feb 2019 02:26:00 GMT"</span>,</span><br><span class="line">      <span class="attr">"images"</span>: [</span><br><span class="line">        &#123;</span><br><span class="line">          <span class="attr">"naturalHeight"</span>: <span class="number">460</span>,</span><br><span class="line">          <span class="attr">"width"</span>: <span class="number">640</span>,</span><br><span class="line">          <span class="attr">"diffbotUri"</span>: <span class="string">"image|3|-1139316034"</span>,</span><br><span class="line">          <span class="attr">"url"</span>: <span class="string">"http://e0.ifengimg.com/02/2019/0219/1731DC8A29EB2219C7F2773CF9CF319B3503D0A1_size382_w690_h460.png"</span>,</span><br><span class="line">          <span class="attr">"naturalWidth"</span>: <span class="number">690</span>,</span><br><span class="line">          <span class="attr">"primary"</span>: <span class="literal">true</span>,</span><br><span class="line">          <span class="attr">"height"</span>: <span class="number">426</span></span><br><span class="line">        &#125;,</span><br><span class="line">        <span class="comment">// ...</span></span><br><span class="line">      ],</span><br><span class="line">      <span class="attr">"author"</span>: <span class="string">"中国新闻网"</span>,</span><br><span class="line">      <span class="attr">"estimatedDate"</span>: <span class="string">"Wed, 20 Feb 2019 06:47:52 GMT"</span>,</span><br><span class="line">      <span class="attr">"diffbotUri"</span>: <span class="string">"article|3|1591137208"</span>,</span><br><span class="line">      <span class="attr">"siteName"</span>: <span class="string">"ifeng.com"</span>,</span><br><span class="line">      <span class="attr">"type"</span>: <span class="string">"article"</span>,</span><br><span class="line">      <span class="attr">"title"</span>: <span class="string">"故宫，你低调点！故宫：不，实力已不允许我继续低调"</span>,</span><br><span class="line">      <span class="attr">"breadcrumb"</span>: [</span><br><span class="line">        &#123;</span><br><span class="line">          <span class="attr">"link"</span>: <span class="string">"https://news.ifeng.com/"</span>,</span><br><span class="line">          <span class="attr">"name"</span>: <span class="string">"资讯"</span></span><br><span class="line">        &#125;,</span><br><span class="line">        &#123;</span><br><span class="line">          <span class="attr">"link"</span>: <span class="string">"https://news.ifeng.com/shanklist/3-35197-/"</span>,</span><br><span class="line">          <span class="attr">"name"</span>: <span class="string">"大陆"</span></span><br><span class="line">        &#125;</span><br><span class="line">      ],</span><br><span class="line">      <span class="attr">"humanLanguage"</span>: <span class="string">"zh"</span>,</span><br><span class="line">      <span class="attr">"meta"</span>: &#123;</span><br><span class="line">        <span class="attr">"og"</span>: &#123;</span><br><span class="line">          <span class="attr">"og:time "</span>: <span class="string">"2019-02-20 02:26:00"</span>,</span><br><span class="line">          <span class="attr">"og:image"</span>: <span class="string">"https://e0.ifengimg.com/02/2019/0219/1731DC8A29EB2219C7F2773CF9CF319B3503D0A1_size382_w690_h460.png"</span>,</span><br><span class="line">          <span class="attr">"og:category "</span>: <span class="string">"凤凰资讯"</span>,</span><br><span class="line">          <span class="attr">"og: webtype"</span>: <span class="string">"news"</span>,</span><br><span class="line">          <span class="attr">"og:title"</span>: <span class="string">"故宫，你低调点！故宫：不，实力已不允许我继续低调"</span>,</span><br><span class="line">          <span class="attr">"og:url"</span>: <span class="string">"https://news.ifeng.com/c/7kQcQG2peWU"</span>,</span><br><span class="line">          <span class="attr">"og:description"</span>: <span class="string">"　　“我的名字叫紫禁城，快要600岁了，这上元的夜啊，总是让我沉醉，这么久了却从未停止。” 　　“重"</span></span><br><span class="line">        &#125;,</span><br><span class="line">        <span class="attr">"referrer"</span>: <span class="string">"always"</span>,</span><br><span class="line">        <span class="attr">"description"</span>: <span class="string">"　　“我的名字叫紫禁城，快要600岁了，这上元的夜啊，总是让我沉醉，这么久了却从未停止。” 　　“重"</span>,</span><br><span class="line">        <span class="attr">"keywords"</span>: <span class="string">"故宫 紫禁城 故宫博物院 灯光 元宵节 博物馆 一票难求 元之 中新社 午门 杜洋 藏品 文化 皇帝 清明上河图 元宵 千里江山图卷 中英北京条约 中法北京条约 天津条约"</span>,</span><br><span class="line">        <span class="attr">"title"</span>: <span class="string">"故宫，你低调点！故宫：不，实力已不允许我继续低调_凤凰资讯"</span></span><br><span class="line">      &#125;,</span><br><span class="line">      <span class="attr">"authorUrl"</span>: <span class="string">"https://feng.ifeng.com/author/308904"</span>,</span><br><span class="line">      <span class="attr">"pageUrl"</span>: <span class="string">"https://news.ifeng.com/c/7kQcQG2peWU"</span>,</span><br><span class="line">      <span class="attr">"html"</span>: <span class="string">"&lt;p&gt;“我的名字叫紫禁城，快要600岁了，这上元的夜啊，总是让我沉醉，这么久了却从未停止。...&lt;/blockquote&gt; &lt;/blockquote&gt;"</span>,</span><br><span class="line">      <span class="attr">"text"</span>: <span class="string">"“我的名字叫紫禁城，快要600岁了，这上元的夜啊，总是让我沉醉，这么久了却从未停止。”\\n“..."</span>,</span><br><span class="line">      <span class="attr">"authors"</span>: [</span><br><span class="line">        &#123;</span><br><span class="line">          <span class="attr">"name"</span>: <span class="string">"中国新闻网"</span>,</span><br><span class="line">          <span class="attr">"link"</span>: <span class="string">"https://feng.ifeng.com/author/308904"</span></span><br><span class="line">        &#125;</span><br><span class="line">      ]</span><br><span class="line">    &#125;</span><br><span class="line">  ]</span><br><span class="line">&#125;</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>可见其返回了如上的内容，是一个完整的 JSON 格式，其中包含了标题、正文、发布时间等等各种内容。 可见，不需要我们配置任何提取规则，我们就可以完成页面的分析和抓取，得来全不费功夫。</p>
                <h2 id="Diffbot-SDK"><a href="#Diffbot-SDK" class="headerlink" title="Diffbot SDK"></a>Diffbot SDK</h2>
                <p>另外 Diffbot 还提供了几乎所有语言的 SDK 支持，我们也可以使用 SDK 来实现如上功能，链接为：<a href="https://www.diffbot.com/dev/docs/libraries/，如果大家使用" target="_blank" rel="noopener">https://www.diffbot.com/dev/docs/libraries/，如果大家使用</a> Python 的话，可以直接使用 Python 的 SDK 即可，Python 的 SDK 链接为：<a href="https://github.com/diffbot/diffbot-python-client。" target="_blank" rel="noopener">https://github.com/diffbot/diffbot-python-client。</a> 这个库并没有发布到 PyPi，需要自己下载并导入使用，另外这个库是使用 Python 2 写的，其实本质上就是调用了 requests 库，如果大家感兴趣的话可以看一下。 下面是一个调用示例：</p>
                <figure class="highlight routeros">
                  <table>
                    <tr>
                      <td class="gutter">
                        <pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre>
                      </td>
                      <td class="code">
                        <pre><span class="line"><span class="keyword">from</span><span class="built_in"> client </span>import DiffbotClient,DiffbotCrawl</span><br><span class="line"></span><br><span class="line">diffbot = DiffbotClient()</span><br><span class="line">token = <span class="string">'your_token'</span></span><br><span class="line">url = <span class="string">'http://shichuan.github.io/javascript-patterns/'</span></span><br><span class="line">api = <span class="string">'article'</span></span><br><span class="line">response = diffbot.request(url, token, api)</span><br></pre>
                      </td>
                    </tr>
                  </table>
                </figure>
                <p>通过这行代码我们就可以通过调用 Article API 来分析我们想要的 URL 链接了，返回结果是类似的。 具体的用法大家直接看下它的源码注释就一目了然了，还是很清楚的。 好，以上便是对智能化提取页面原理的基本介绍以及对 Diffbot 的用法的讲解，后面我会继续介绍其他的智能化解析方法以及一些相关实战，希望大家可以多多关注。</p>
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